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EN
Motivated by the two-step directional Newton method considered by Argyros and Hilout (2010) for approximating a zero X* of a differentiable function F defined on a convex set D of a Hilbert space H, we consider a two-step Newton–Lavrentiev method (TSNLM) for obtaining an approximate solution to the nonlinear ill-posed operator equation F(x)=f, where F : D(F) ⊆ X → X is a nonlinear monotone operator defined on a real Hilbert space X. It is assumed that F(∧x)=f and that the only available data are fδ with ||f- fδ||≤δ. We prove that the TSNLM converges cubically to a solution of the equation F(x)+α(x-x0)= fδ (such solution is an approximation of ∧x) where x0 is the initial guess. Under a general source condition on x0-∧x, we derive order optimal error bounds by choosing the regularization parameter α according to the balancing principle considered by Perverzev and Schock (2005). The computational results provided endorse the reliability and effectiveness of our method.
EN
It is proved that there exist common fixed points for monotone operators in locally convex spaces with a cone.
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